Email this article 
Open Access
Subscription or Fee Access
An Efficient Technique for Detection and Extraction of Animated Region from Gif Images
Abstract
The goal of the project is to implement an approach and test the region Extraction of animated scene in gif images, and to discover how the new developed technique is efficient for it. Under variations of any type or size of natural animated image is difficult task in image mining research area. The aim of this research is to solve this problem by introducing a new approach called An Efficient Technique for Detection and Extraction of Animated Region from gif Images which is more efficient for region extraction and edge detection of animated scenes in to any type of graphical interchanged format image connected component and edge based. Here we are using the edge based region extraction procedure which help in detecting and extraction of animated portion and also in determining the precision rate and recall rate of animated scenes of any animated image .
Keywords
References
Xiaoqing Liu and Jagath Samarabandu, An Edge-based text region extraction. algorithm for Indoor mobile robot navigation, Proceedings of the IEEE, July 2005.
Xiaoqing Liu and Jagath Samarabandu, Multiscale edge-based Text extraction from Complex images, IEEE, 2006.
Julinda Gllavata, Ralph Ewerth and Bernd Freisleben, A Robust algorithm for Text detection in images, Proceedings of the 3rd international symposium on Image and Signal Processing and Analysis, 2003.
Keechul Jung, Kwang In Kim and Anil K. Jain, Text information extraction in images and video: a survey, The journal of the Pattern Recognition society, 2004.
Kongqiao Wang and Jari A. Kangas, Character location in scene images from digital camera, The journal of the Pattern Recognition society, March 2003.
K.C. Kim, H.R. Byun, Y.J. Song, Y.W. Choi, S.Y. Chi, K.K. Kim and Y.K Chung, Scene Text Extraction in Natural Scene Images using Hierarchical Feature Combining and verification, Proceedings of the 17th International Conference on Pattern Recognition (ICPR ’04), IEEE.
Victor Wu, Raghavan Manmatha, and Edward M. Riseman, TextFinder: An Automatic System to Detect and Recognize Text in Images, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 11, November 1999.
Xiaoqing Liu and Jagath Samarabandu, A Simple and Fast Text Localization Algorithm for Indoor Mobile Robot Navigation, Proceedings of SPIE-IS&T Electronic Imaging, SPIE Vol. 5672, 2005.
Qixiang Ye, Qingming Huang, Wen Gao and Debin Zhao, Fast and Robust text detection in images and video frames, Image and Vision Computing 23, 2005.
Rainer Lienhart and Axel Wernicke, Localizing and Segmenting Text in Images and Videos, IEEE Transactions on Circuits and Systems for Video Technology, Vol.12, No.4, April 2002.
Qixiang Ye, Wen Gao, Weiqiang Wang and Wei Zeng, A Robust Text Detection Algorithm in Images and Video Frames, IEEE, 2003.
http://images.google.com
Petrushin, V.A., Emotion Recognition in Speech Signal: Experimental Study, Development, and Application, Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), Beijing, 2000. Vol. IV.
Maybury M.T. (Ed.) Intelligent Multimedia Information Retrieval, AAAI Press/MIT Press, Menlo Park, CA / Cambridge, MA, 1997.
Schutze, H., Automatic word sense discrimination. Computational Ling., Vol 24, 1998, pp. 97–124.
Zhang Ji, Wynne Hsu, Mong Li Lee. Image Mining: Issues, Frameworks and Techniques, in Proc. of the Second International Workshop on Multimedia Data Mining (MDM/KDD'2001), San Francisco, CA, USA, 2001.
Mathswork.com for using the matlab software system. R2007, 7.5.
Zhang J., W. Hsu and M. L. Lee. An Information-driven Framework for Image Mining, in Proc. of 12th International Conference on Database and Expert Systems Applications, Munich, 2001.
Wold E., T. Blum, D. Keislar, and J. Wheaton, Content-based classification, search and retrieval of audio, IEEE Multimedia Magazine, vol. 3,1996.
Wang Y., Z. Liu, and J.-C. Huang, Multimedia Content Analysis, IEEE Signal Processing Magazine, Nov. 2000. Success, 3rd International Conference on Music.
Rosenfeld A., D. Doermann, D. DeMenthon, Eds., Video Mining, Kluwer, 2003.
Boresczky J. S. and L. A. Rowe, A comparison of video shot boundary detection techniques,Storage & Retrieval for Image and Video Databases IV, Proc. SPIE 2670, 1996.
Ardizzone E. and M. Cascia. Automatic video database indexing and retrieval. Multimedia Tools and Applications, Vol. 4, 1997.
Yu H. and W.Wolf. A visual search system for video and image databases. In Proc. IEEE Int’l Conf. On Multimedia Computing and Systems, Ottawa, Canada, June 1997.
Zhang, H.J., Low, C.Y., Smoliar, S.W. and Wu, J.H., Video parsing, retrieval and browsing: an integrated and content-based solution, Proc. ACM Multimedia '95.
H.Chidiac, D.Ziou, “Classification of Image Edges”,Vision Interface’99, Troise-Rivieres, Canada, 1999.pp. 17-24.
Q.Ji, R.M.Haralick, “Quantitative Evaluation of Edge Detectors using the Minimum Kernel Variance Criterion”, ICIP 99. IEEE International Conference on Image Processing volume: 2, 1999, pp.705-709.
Albovik,“Handbook of Image and Video Processing”,Academic Press, 2000.
M.Woodhall, C.Linquist, “New Edge Detection Algorithms Based on Adaptive.
Fundamentals of Image Processing I.T. Young .J. Gerbrands L.J. van Vliet.
Extraction of Text Regions in Natural Images Sneha Sharma Dr. Roxanne Canosa.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.